1. Overview
1.1 Introduction
In this take-home exercise, the economic of the city of Engagement, Ohio USA will be revealed by using appropriate static and interactive statistical graphics methods.
With reference to Challenge 3 Question 1 of VAST Challenge 2022, the following questions will be addressed:
Over the period covered by the dataset, which businesses appear to be more prosperous? Which appear to be struggling? Describe your rationale for your answers.
1.2 Methodology
It is observed from the datasets provided by VAST Challenge 2022 that there are three types of businesses present in Engagement, Ohio USA, namely:
- Workplaces/Employers;
- Restaurants;
- Pubs
In this exercise, the robustness of different types of businesses will be evaluated by different criteria as the data available is different.
Workplaces
For workplaces, data is available on employees, jobs provided, wages, educational level requirement and etc. For restaurants and pubs, data is available on prices, customers’ visits, spending and etc. Therefore, in this exercise, workplaces will be evaluated base on two main criteria:
- Number of employees;
- Wages offered.
Restaurants and Pubs
On the other hand, restaurants and pubs will be evaluated based on:
- Frequency and number of participants’ visits;
- Revenue (participants’ spending)
According to the dataset descriptions provided by VAST Challenge, all restaurants have a Prix Fixe food cost for participants to dine in and all pubs have a hourly cost to visit the pub. Therefore, assuming all visits to restaurants are for dinning, restaurants’ revenue will be calculated by number of visits times Prix Fixe food cost. Similarly, pubs’ revenue will be calculated by duration of visits times hourly cost of visits.
Alternative approach of deriving balance difference before and after restaurants and pubs visits as spending is considered but not preferred as balance differences are inconsistent and could be due to unknown reasons.
2. Data Preparation
2.1 Install and Load Packages
The following code chunk installs the required R packages and loads them onto RStudio environment.
show
packages = c('ggiraph', 'plotly', 'DT', 'patchwork',
'gganimate', 'tidyverse','readxl', 'gifski',
'gapminder', 'treemap', 'treemapify', 'rPackedBar',
'trelliscopejs', 'zoo', 'd3treeR', 'ggridges')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
2.2 Import Raw Datasets
Relevant datasets are imported using read_csv() of readr
package, which is useful for reading delimited files into tibbles.
show
jobs <- read_csv('rawdata/Jobs.csv')
pubs <- read_csv('rawdata/Pubs.csv')
restaurants <- read_csv('rawdata/Restaurants.csv')
travel <- read_csv('rawdata/TravelJournal.csv')
The following code chunk is used to have an overview of the datasets.
2.3 Data Wrangling
2.3.1 Workplace
File jobs is cleaned by renaming values for ease of
reading. A new file jobsedu is created using
group_by() to reveal data on jobs offered for different
education requirements.
show
jobs$educationRequirement <- sub('HighSchoolOrCollege',
'High School or College',
jobs$educationRequirement)
2.3.2 Restaurants and Pubs
The following code chunk extracts travel records related to
restaurants and pubs using filter() and derives spending of
each visit using inner_join() and
mutate().
Datasets are also cleaned by removing irrelevant columns using
select() and renaming column names using
rename() for ease of understanding.
show
restaurantstr <- travel %>%
filter(purpose == 'Eating') %>%
mutate(travelTime = travelEndTime - travelStartTime) %>%
select(-c(travelStartTime:travelEndTime, endingBalance)) %>%
inner_join(y= restaurants,
by = c('travelEndLocationId'= 'restaurantId')) %>%
mutate(visitDuration = checkOutTime - checkInTime) %>%
select(-c(purpose, location, checkOutTime)) %>%
rename('restaurantId' = 'travelEndLocationId',
'spending' = 'foodCost')
pubstr <- travel %>%
filter(purpose == 'Recreation (Social Gathering)') %>%
mutate(travelTime = travelEndTime - travelStartTime) %>%
select(-c(travelStartTime: travelEndTime, endingBalance)) %>%
inner_join(y= pubs,
by = c('travelEndLocationId'= 'pubId')) %>%
mutate(visitDuration = checkOutTime - checkInTime,
spending = as.numeric(visitDuration/60)* hourlyCost) %>%
select(-c(purpose, location, checkOutTime)) %>%
rename('pubId' = 'travelEndLocationId')
The following code chunk is used to check for missing values.
2.4 Save as and Read RDS Files
The cleaned datasets are saved and read in RDS format to avoid uploading large files to Git.
3. Visualizations and Insights
3.1 Workplaces
3.1.1 Number of Jobs Provided by Each Workplace
show
jobsnum <- jobs %>%
group_by(employerId) %>%
summarise(jobNum = n(),
totalPay = sum(hourlyRate),
avgPay = mean(hourlyRate))
tooltip_css <- 'background-color: #008080;
font-stype: bold; color: white'
jobsnum$tooltip <- c(paste0('Employer ID: ', jobsnum$employerId,
'\n Number of Employees: ', jobsnum $jobNum))
p <- ggplot(data = jobsnum, aes(x= jobNum)) +
geom_dotplot_interactive(aes(tooltip = tooltip),
fill = '#bada55',
stackgroups = TRUE,
binwidth = 0.1,
color = NA,
method = 'histodot') +
scale_y_continuous(NULL, breaks = NULL) +
scale_x_continuous(limits = c(1, 10),
breaks = c(1,2,3,4,5,6,7,8,9,10),
labels = c(1,2,3,4,5,6,7,8,9,10)) +
labs(x= 'Number of Employees',
title = "How Many Jobs Is Each Workplace Provding?",
subtitle= 'Economic in Engagement, Ohio',
caption = "Source: VAST Challenge 2022") +
theme(panel.grid.major = element_line(color= 'grey', size = 0.1),
panel.background= element_blank(),
axis.line= element_line(color= 'grey'),
plot.caption = element_text(hjust = 0))
girafe(ggobj = p,
width_svg = 8,
height_svg = 8*0.618,
options = list(opts_tooltip(css = tooltip_css)))
3.1.2 Employee Hourly Pay by Workplace
show
show
d3tree(tm, rootname = 'Employee Hourly Pay by Workplace')
3.1.3 Job Pay by Education Requirement
show
jobsedu <- jobs %>%
group_by(employerId, educationRequirement) %>%
summarise(jobnum = n(),
avgHourlyPay = round(mean(hourlyRate),2),
totalHourlyPay = sum(hourlyRate)) %>%
rename('Average Hourly Pay' = 'avgHourlyPay')
jobsedu1 <- filter(jobsedu, educationRequirement=='Low')
jobsedu2 <- filter(jobsedu, educationRequirement=='High School or College')
jobsedu3 <- filter(jobsedu, educationRequirement=='Bachelors')
jobsedu4 <- filter(jobsedu, educationRequirement=='Graduate')
show
p1 <- plotly_packed_bar(input_data = jobsedu1,
label_column = 'employerId',
value_column = 'Average Hourly Pay',
number_rows = 10,
plot_title = 'Top 10 Workplaces for Low Education - by average hourly pay',
xaxis_label = 'Average Hourly Pay',
hover_label = 'Average Hourly Pay',
min_label_width = 0.001,
color_bar_color = '#66cdaa',
label_color = 'white')
plotly::config(p1, displayModeBar = FALSE)
show
p2 <- plotly_packed_bar(input_data = jobsedu2,
label_column = 'employerId',
value_column = 'Average Hourly Pay',
number_rows = 10,
plot_title = 'Top 10 Workplaces for Low Education - by average hourly pay',
xaxis_label = 'Average Hourly Pay',
hover_label = 'Average Hourly Pay',
min_label_width = 0.001,
color_bar_color = '#66cdaa',
label_color = 'white')
plotly::config(p2, displayModeBar = FALSE)
show
p3 <- plotly_packed_bar(input_data = jobsedu3,
label_column = 'employerId',
value_column = 'Average Hourly Pay',
number_rows = 10,
plot_title = 'Top 10 Workplaces for Low Education - by average hourly pay',
xaxis_label = 'Average Hourly Pay',
hover_label = 'Average Hourly Pay',
min_label_width = 0.001,
color_bar_color = '#66cdaa',
label_color = 'white')
plotly::config(p3, displayModeBar = FALSE)
show
p4 <- plotly_packed_bar(input_data = jobsedu4,
label_column = 'employerId',
value_column = 'Average Hourly Pay',
number_rows = 10,
plot_title = 'Top 10 Workplaces for Low Education - by average hourly pay',
xaxis_label = 'Average Hourly Pay',
hover_label = 'Average Hourly Pay',
min_label_width = 0.001,
color_bar_color = '#66cdaa',
label_color = 'white')
plotly::config(p4, displayModeBar = FALSE)
3.2 Restaurants and Pubs
3.2.1 Restaurants Monthly Customer Visits
show
restaurants <- restaurants %>%
mutate(yearmonth = as.yearmon(checkInTime))
restaurantsv <- restaurants %>%
group_by(restaurantId, yearmonth) %>%
summarise(visits = n(),
revenue = sum(spending),
price = mean(spending))
r <- ggplot(restaurantsv, aes(x= as.factor(yearmonth), y= visits)) +
geom_col(fill= '#008080') +
labs(x= 'Month Year', y= 'Number of\nCustomer\nVisits',
title = 'Monthly Customer Visits by Restaurant') +
facet_trelliscope(~ restaurantId,
nrow = 2, ncol = 2, width = 1000,
path = 'trellisr/',
self_contained = TRUE) +
theme(axis.title.y= element_text(angle=0),
axis.ticks.x= element_blank(),
panel.background= element_blank(),
axis.line= element_line(color= 'grey'))
r
3.2.2 Pubs Monthly Customer Visits
show
pubs <- pubs %>%
mutate(yearmonth = as.yearmon(checkInTime))
pubsv <- pubs %>%
group_by(pubId, yearmonth) %>%
summarise(visits = n(),
revenue = sum(spending),
price = mean(hourlyCost))
pub <- ggplot(pubsv, aes(x= as.factor(yearmonth), y= revenue)) +
geom_col(fill= '#008080') +
labs(x= 'Month Year', y= 'Number of\nCustomer\nVisits',
title = 'Monthly Customer Visits by Pub') +
facet_trelliscope(~ pubId,
nrow = 2, ncol = 2, width = 1000,
path = 'trellisp/',
self_contained = TRUE) +
theme(axis.title.y= element_text(angle=0),
axis.ticks.x= element_blank(),
panel.background= element_blank(),
axis.line= element_line(color= 'grey'))
pub
4. Conclusions
Learning Points